Data were calculated using the male and female populations of North
Rhine Westphalia, Germany, as the reference populations. Obese patients
had a body mass index of 25 to 75 kg/m2. Error bars
represent 95% confidence limits. A test for trend was significant
(P<.001) for both sexes.

Figure 2. Standardized Mortality Ratios for Obese Patients by
Age and BMI Groups

Context The effect of age on excess mortality from all
causes associated with obesity is controversial. Few studies have
investigated the association between body mass index (BMI, calculated
as weight in kilograms divided by the square of height in meters), age,
and mortality, with sufficient numbers of subjects at all levels of
obesity.

Objective To assess the effect of age on the excess mortality
associated with all degrees of obesity.

Design Prospective cohort study.

Setting and Participants A total of 6193 obese patients with mean
(SD) BMI of 36.6 (6.1) kg/m2 and mean (SD) age of 40.4
(12.9) years who had been referred to the obesity clinic of
Heinrich-Heine University, Düsseldorf, Germany, between 1961 and
1994. Median follow-up time was 14.8 years.

Main Outcome Measure All-cause mortality through 1994 among 6053
patients for whom follow-up data were available (1028 deaths) analyzed
as standardized mortality ratios (SMRs) using the male-female
population of the geographic region (North Rhine Westphalia) as
reference.

Results The cohort was grouped into approximate quartiles
according to age (18-29, 30-39, 40-49, and 50-74 years) and BMI (25 to
<32, 32 to <36, 36 to <40, and ≥40 kg/m2) at
baseline. The SMRs showed a significant excess mortality with an SMR
for men of 1.67 (95% confidence interval, 1.51-1.85;
P<.001) and an SMR for women of 1.45 (95% confidence
interval, 1.34-1.57; P<.001). The excess mortality
associated with obesity declined with age. For men, the SMRs of the 4
age groups were 2.46, 2.30, 1.99, and 1.31, respectively; for women,
they were 1.81, 2.10, 1.70, and 1.26, respectively (Poisson trend test,
P<.001). The SMRs increased with BMI but, within each BMI
group, the SMRs decreased with age. The lowest SMRs (for men, 1.01; for
women, 0.91) were obtained for patients older than 50 years with BMIs
of 25 to less than 32 kg/m2. Thus, older men and women at a
BMI range of 25 to less than 32 kg/m2 had no excess
mortality. The highest SMRs (for men, 4.22; for women, 3.79) were
calculated for the patients aged 18 to 29 years with a BMI of 40
kg/m2 or higher.

Conclusions In this large cohort of obese persons, risk of
death increased with body weight, but obesity-related excess mortality
declined with age at all levels of obesity.

The association
between body weight and mortality remains controversial.1
Although in general, a J- or U-shaped relationship is accepted,
questions remain about the degree of excess mortality associated with
obesity and the impact of age on the relationship between overweight
and mortality.1 Most studies about this issue have shown a
decrease of the obesity-related excess risk with increasing
age,2- 12 implicating a relatively high optimal body mass
index (BMI, calculated as weight in kilograms divided by the square of
height in meters) of about 30 kg/m2 associated with minimum
mortality in older persons.13 However, only a few studies
have had sufficient size to describe in detail the dependence of
mortality on body weight and age. Recently, Stevens et al14
suggested that the relative risk (RR) associated with greater body
weight declined with age in adults older than 30 years. The study
cohort consisted of participants of the American Cancer Society's
Cancer Prevention Study, with a BMI distribution similar to that of a
normal population. The mean BMI was 25 kg/m2 and the
largest BMI category chosen was that of at least 32 kg/m2.
Hence, it was impossible to investigate the effect of age on the excess
mortality associated with higher degrees of obesity. There are very few
studies investigating the mortality of extremely obese
subjects.9

In this article, the associations among body weight, age, and
mortality are investigated by using the data of the Düsseldorf
Obesity-Mortality Study (DOMS).15 In this study,
a large cohort of obese patients, including a considerable number of
grossly obese (BMI, 32 to <40 kg/m2) and
morbidly obese (BMI, ≥40 kg/m2) individuals, was
recruited during a period of 33 years and followed up for a median of
14.8 years. The excess mortality associated with several degrees
of obesity was assessed by means of a comparison of the study cohort
with the general population living in the same geographic
area.15 Here, we present an analysis for different age
groups to assess the effect of age on the excess mortality associated
with several degrees of obesity.

METHODS

Subjects and Data

The DOMS is a prospective cohort study of 6193 obese patients
(1591 men and 4602 women) who had been referred to the obesity clinic
of Heinrich-Heine University, Düsseldorf, Germany, between 1961
and 1994. In general, the referral of overweight patients to the
obesity clinic was made by their general practitioners to involve them
in a 4184-J/d dietary treatment plan that included elements of group
therapy and behavior modification.16 Some patients were
also referred by the endocrine clinic at Heinrich-Heine University or
by surgical departments to achieve weight loss before elective surgery.
All services of the obesity clinic were provided free of charge to the
patient.

The initial medical examination included a history taking,
physical examination, and clinical chemistry. The design and data
collection have been described in detail previously.15 In
short, the following baseline data were collected at the initial
medical examination: date of examination, name, current address, sex,
date of birth, height, weight, blood pressure, glucose tolerance, and
cholesterol level. In addition, since 1977, information on smoking
habits has been collected systematically. Inclusion criteria were age
at entry of 18 to 74 years and a BMI of at least 25 kg/m2.
Height and weight were measured with the patients in light clothes
(shirts and trousers or skirts), without shoes. Blood pressure was
measured by means of a mercury sphygmomanometer; serum cholesterol
and blood glucose levels were measured by routine clinical chemistry
methods, as described previously.17

Glucose tolerance was assessed by measuring the capillary blood glucose
level 2 hours after a 100-g oral glucose load was given after an
overnight fast. A patient was classified as having diabetes if the
diagnosis of diabetes was previously known, if the fasting blood
glucose level was at least 6.7 mmol/L (120 mg/dL), or if the glucose
tolerance test yielded a capillary blood glucose level of at least 11.1
mmol/L (200 mg/dL). A patient was classified as having
impaired glucose tolerance if the glucose tolerance test yielded 2-hour
capillary blood glucose values of at least 7.8 mmol/L (140 mg/dL) and
less than 11.1 mmol/L (200 mg/dL). After 1972, all patients signed an
agreement that their data could be used in the context of scientific
studies. The impact of blood pressure, cholesterol, glucose tolerance,
and smoking on mortality was investigated previously.15 In
this article, we concentrate on the effect of age on the excess
mortality associated with obesity.

Mortality Follow-up

Vital status was ascertained from municipal residents' registries.
Vital status up to 1994 could be determined for 5775 patients (93.3%).
Additionally, the vital status of 278 patients up to any point earlier
than 1994 was obtained from former follow-up investigations or removal
dates. Thus, we could calculate patient-year data based on 6053
patients (97.7%). We could not obtain survival data for only 140
patients (2.3%). All available survival data were used as either event
or censored observation. The patients' survival time was principally
calculated on a daily basis.

Statistical Analysis

To investigate the associations among weight, age, and mortality, we
grouped the study population approximately into quartiles according to
age at baseline (group 1, 18-29; group 2, 30-39; group 3, 40-49; and
group 4, 50-74 years) and BMI (group 1, 25 to <32; group 2, 32 to
<36; group 3, 36 to <40; and group 4, ≥40 kg/m2). Body
mass index of at least 25 but less than 32 kg/m2 (group 1)
was referred to as moderate obesity; BMI between 32 and less
than 40 kg/m2 (groups 2 and 3) was gross obesity;
and BMI of 40 kg/m2 or more (group 4) was morbid
obesity.

We calculated standardized mortality ratios (SMRs)18
separately for men and women within the 4 age groups and the 16
age-by-BMI groups by using the male and female populations of North
Rhine Westphalia, Germany, as reference populations, respectively. The
SMRs permit a comparison of the mortality of the obese study population
with the mortality of a complete general population living in the same
geographic area. The mortality ratios were standardized in an indirect
manner18 according to age and calendar year, using 1-year
intervals. The population and mortality data of North Rhine Westphalia
for age and calendar year were made available on a 1-year basis from
the State Office for Data Processing and Statistics of North Rhine
Westphalia, Düsseldorf. Significance tests and 95% confidence
intervals for the SMRs were calculated by using the Byar approximation
to the exact Poisson test and exact Poisson limits.18
Whether there was a trend in the SMRs across age groups was
investigated by means of the Poisson trend test.18

To quantify and compare the impact of sex, age, and BMI on the excess
mortality associated with obesity, multiple linear regression was used.
In this model, the estimated SMRs served as dependent variables and
sex, mean age, and mean BMI of the corresponding age-by-BMI groups were
used as explanatory factors. The risk factors systolic and diastolic
blood pressure, cholesterol level, impaired glucose tolerance,
diabetes, and smoking were also investigated. To take the
heteroscedasticity of the SMRs into account, the weighted least squares
method was applied, using the SEs of the SMRs as weights. It should be
noted that such a simple model cannot be used to describe the
relationship between excess mortality and the considered factors in
detail. However, the estimated regression coefficients give a rough
impression of the impact of sex, age, and BMI on the excess mortality,
because they
represent the average change in SMR associated
with a change of 1 unit in the explanatory factors.

For computations, the SAS procedures UNIVARIATE,19
MEANS,19 FREQ,20 and REG20 were
used. Standardized mortality ratios and corresponding P values
and confidence intervals were calculated by means of programs written
in matrix language using SAS/IML.21

RESULTS

Patients were recruited between 1961 and 1994 and followed up for a
median of 14.8 years (mean [SD], 14.3 [8.2]; range, 0-33;
interquartile range, 7.3-20.2 years). The range for BMI was 25.0 to
74.4 kg/m2 (mean [SD], 36.6 [6.1] kg/m2)
and for age was 18 to 74 years (mean [SD], 40.4 [12.9] years). Up
to 1994, 1028 patients (16.6%) died (365 men and 663 women). The total
number of observed patient-years was 87,179 (for men, 21,932; for women,
65,247). The crude mortality rate was 11.79 deaths per 1000 patient-years
(for men, 16.64; for women, 10.16). A descriptive analysis of the baseline
data within the 4 age groups is shown in
Table 1.

The estimated SMRs for men and women within age and age-by-BMI groups
are shown in Table 2,
Table
3, Figure
1, and Figure
2. Overall and within the BMI groups, the
SMRs for both sexes tended to decrease with age. Due to the limited
event numbers, especially in the lower age groups, the SMRs dependent
on age and BMI show some irregularities. Overall, in women as well as
in some BMI groups for men and women, the SMRs for the first age group
were lower than for the second. However, the number of deaths in the
first age group was very low, causing the confidence intervals to be
wide. Hence, these results do not rule out a general decreasing trend
in SMRs across age groups. Overall, the decreasing trend of SMRs across
age groups was statistically significant in both sexes
(P<.001). A substantial excess mortality (SMR >1.5) was
observed for moderately obese men (BMI, 25 to <32
kg/m2) who were younger than 40 years, for men
with a BMI of at least 32 but less than 36 kg/m2 who were
younger than 50 years, and for men with a BMI of 36 kg/m2
or more. For women, SMRs higher than 1.5 were obtained only in morbidly
obese patients (BMI ≥40 kg/m2) and those with a BMI of 36
to less than 40 kg/m2 who were younger than 40 years.
Assuming that the very low SMR of 0.84 in the group of women aged 18 to
29 years with a BMI of at least 36 but less than 40 kg/m2
is due to random error, the lowest
excess mortality was found for moderately obese
men (SMR, 1.01) and women (SMR, 0.91) who were aged 50 years or older.
The highest excess mortality was observed for morbidly obese men (SMR,
4.22) and women (SMR, 3.79) who were younger than 30 years.

In the multiple linear regression analysis with the estimated SMRs as
values of the dependent variable, sex (β=.56;
P=.02), age (β=−.03;
P=.007), and BMI (β=.13;
P<.001) were, as expected, significantly associated with
SMR. Because none of the possible interactions were significant, the
model containing only main effects was used. With this simple model, it
was not possible to show additional significant effects of the other
risk factors (blood pressure, cholesterol level, impaired glucose
tolerance, diabetes, and smoking) on the obesity-related excess
mortality. However, the regression coefficients for age and BMI did not
change significantly when one of the other risk factors was included in
the model. The coefficient of determination for the model was
R2=0.697; ie, sex, age, and BMI
explained about 70% of the variance of the SMRs. The inverses of the
regression coefficients give the average change of units in the
explanatory factors associated with a change of 1.0 in the SMRs (eg,
from 1.5 to 2.5). Multiplying the inverses of the regression
coefficients of age and BMI by the regression coefficient of sex yields
the difference of 19.1 years for age and 4.3 kg/m2 for BMI,
which have the same effect on excess mortality as sex (ie, an SMR
increase of 0.555).

COMMENT

This study represents by far the largest mortality follow-up of a
cohort of obese patients, including a considerable number of grossly
obese (BMI of 32 to <40 kg/m2) and morbidly obese (BMI
≥40 kg/m2) subjects. We have compared the mortality rates
of this cohort of obese patients with the general population of North
Rhine Westphalia, Germany, stratified for sex, age, and BMI and
standardized for age and
calendar year. Although in total more than 6000
obese patients were recruited for a period of 33 years, the capacity to
describe mortality in terms of dependence on sex, age, and BMI is
limited. Considering 2 sexes, 4 age groups, and 4 BMI groups, the
entire cohort is subdivided into 32 groups (2 × 4 ×
4=32) of limited size. As expected, relatively few
deaths were observed in the lower age ranges, leading to uncertain SMR
estimates in these groups. Hence, for the estimated excess mortality
associated with obesity dependent on age, some irregularities can be
expected. The 32 SMR estimates must be interpreted simultaneously,
taking the significant overall decreasing trend of the SMRs with age
for both sexes into account.

Additional limitations of our study should be considered. First, the
study cohort does not represent a random sample of the obese
population; hence, recruitment bias may be a problem. Physician and
self-referral patterns may vary among subgroups of the study cohort
(eg, men vs women, age group, concomitant diseases or symptoms, health
beliefs of patients and their physicians, or extent of obesity). It is
fair to assume, however, that those obese patients referred to our
obesity clinic tended to be more concerned about their health or had
more problems concerning obesity-associated symptoms and diseases than
the remaining population of grossly obese patients living in the same
geographic area. This interpretation is supported by the high mean
values of blood pressure and the high proportion of people with
diabetes and impaired glucose tolerance in our cohort.

Second, information on putative confounders, such as smoking, alcohol
consumption, medication, body fat distribution, obesity-associated
symptoms, and psychological variables, as well as the patients' social
status and physical activity level, was insufficient to be included in
the analysis of this large cohort recruited during a period of 33
years.

Third, no systematic information is available regarding the course of
obesity after recruitment of the patients. Thus, the possible effects
of weight change could not be investigated. However, earlier analyses
of subgroups of patients indicate that the overall long-term effect on
weight reduction by our obesity clinic intervention was almost
negligible; as in other comparable reports, approximately 50% of
patients did not attend the therapeutic program after the initial
examination and, for the remaining patients, a mean weight loss of
about 9 kg was reached after a mean duration of 6 months. However, a
significant long-term weight reduction was demonstrable in less than
5% of patients.16

Fourth, it was not possible to obtain reliable information on cause of
death. According to the prevailing state law of North Rhine Westphalia,
access was not available to the death certificates of the patients in
this study.

Finally, it can be argued that SMR may decrease with increasing age
simply because of an increase of the prevalence of obesity in the
general population with increasing age.22 Indeed, the
population-based German Cardiovascular Prevention (GCP) Study
demonstrated that the average BMI rises steadily with age in
Germany.23 We have estimated from the data of the GCP
Study24 that the prevalences of gross and morbid obesity
(BMI ≥32 kg/m2) in North Rhine Westphalia for the age
groups of less than 30, 30 to 39, 40 to 49, and 50 or more years are
about 4.2%, 5.9%, 8.8%, and 9.5% for men and 3.8%, 6.0%, 9.6%,
and 20.4% for women, respectively. Thus, the prevalence of gross and
morbid obesity increases by a factor of 2.3 in men and 5.4 in women
when comparing subjects who were aged 50 years or older with those
subjects younger than 30 years. To investigate the possible bias of the
SMR due to the increasing prevalence of obesity with increasing age, we
calculated the SMRs of gross and morbid obesity within the 4 age groups
and compared the results with the RRs of exposed vs unexposed persons,
estimated by means of the formula proposed by Jones and
Swerdlow.22 The
SMRs for the 4 age groups were 2.6, 2.3, 2.2,
and 1.4 for men and 2.1, 2.3, 1.9, and 1.3 for women, respectively. The
prevalence-adjusted RRs were 2.8, 2.5, 2.5, and 1.5 for men and 2.2,
2.5, 2.1, and 1.5 for women, respectively. Thus, after adjusting for
the increasing prevalence of obesity, the decreasing trend of excess
mortality related to gross and morbid obesity with increasing age
did not disappear. Even the amount of excess risk decrease
remained unchanged. Thus, we conclude that the observed decrease of the
SMRs with increasing age cannot be attributed to the increasing
prevalence of obesity with increasing age in the general population of
North Rhine Westphalia. The SMRs represent estimations of the excess
risk standardized for age and calendar year of the defined age-by-BMI
groups with respect to the general population having a specific BMI
distribution. One might be interested in estimating RRs of the study
cohort with respect to a BMI-truncated section of the general
population (eg, BMI <20 or <25 kg/m2). For an
appropriate estimation of these RRs, the population and mortality data
of the BMI-truncated section of the general population are
required. Such data, however, are not available.

Despite these limitations, our data complement recent results
indicating that the RR associated with greater body weight declined
with age in adults having a BMI distribution similar to that of a
normal population.14 Our data suggest that the excess
mortality associated with greater BMI declined considerably with age in
all degrees of obesity. This finding is plausible, given that a similar
trend has been found in other conditions related to cardiovascular
mortality, such as type 2 diabetes25 and
hypercholesterolemia.26 Although the decrease of excess
mortality with increasing age among extremely obese men was already
suggested by Drenick et al,2 no reliable estimates of
excess mortality could be presented because of the low sample size; the
estimated 12-fold excess mortality of obese men in subjects aged 25 to
34 years was based on only 3 deaths and may therefore represent an
overestimation. Our data had sufficient size to show a significant
decreasing trend of the SMRs with age in obese men and women, with a
more reliable quantification of the excess mortality associated with
obesity dependent on age. Especially in morbid obesity (BMI ≥40
kg/m2), the excess mortality declined with age, from 4.2 to
1.9 for men and from 3.8 to 1.8 for women, when comparing the age group
younger than 30 years with that of 50 years or older.

It is evident that the risk of death does not increase with BMI in
elderly people. A simple argument is that the absolute risk of death
increases with age and finally reaches 100%. Thus, it cannot be
further increased by factors other than age. It has been suggested that
obesity has some impact on mortality up to 80 years of age but not
thereafter.9 Our data provide information about the amount
of excess mortality associated with several degrees of obesity in
relation to sex and age. It could be estimated that to be male (in
comparison with women at the same age and BMI), to be aged 19 years or
younger (at the same BMI), or to have a BMI of about 4
kg/m2 more (at the same age) increases the excess mortality
by approximately the same extent. According to these estimations, obese
women can have a BMI of about 4 kg/m2 more than obese men,
with approximately the same excess risk of death compared with the
expected mortality for the general population. An increase in BMI of
about 2.25 kg/m2 per decade of life allows obese men and
women to maintain a constant level of excess risk. For a 170-cm tall
person, this would translate to a weight increase of about 6.5 kg per
decade of life. This value is higher than the increased mean weight
allowance of 4.5 kg per decade of life that is based on actuarial
data.27 Nevertheless, there seems to be no considerable
difference between these results because the weight allowance per
decade of life is probably not constant in all ranges of obesity and
age. Although these estimates can give only a rough impression about
the strength of the effects of sex, age, and BMI on mortality in
obesity, they should have important consequences for rating systems in
the field of health insurance policies.

No excess mortality was associated with a BMI of at least 25 but less
than 32 kg/m2 in the group aged 50 years or older for
either men or women. These results are consistent with previous
findings in a sample of the US population that the range of BMI with
minimum mortality and low excess risk (<20%) is wide and includes
70% of the population.28 Our study gives additional
information about the excess risk of higher BMI groups dependent on
age. A low excess mortality (SMR <1.2) was observed for women aged 50
years or older with a BMI of less than 40 kg/m2 and for men
aged 50 years or older with a BMI of less than 36
kg/m2. In practice, such patients are often advised to
lose weight even if they do not have any obesity-related diseases, such
as type 2 diabetes, hypertension, and hypercholesterolemia; however,
such dieting might unjustifiably decrease their perceived quality of
life.

In summary, the excess mortality associated with obesity declined
considerably with age for both sexes in all degrees of obesity.